scholarly journals Wavelet Adaptive Algorithm and Its Application to MRE Noise Control System

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Zhang Yulin ◽  
Zhao Xiuyang
Keyword(s):  
Control System ◽  
Adaptive Algorithm ◽  
Noise Control ◽  
Complex Modulus ◽  
Active Noise Control ◽  
Adaptive Algorithms ◽  
Lms Algorithm ◽  
Magnetorheological Elastomers ◽  
Active Noise ◽  
Simulation Results

To address the limitation of conventional adaptive algorithm used for active noise control (ANC) system, this paper proposed and studied two adaptive algorithms based on Wavelet. The twos are applied to a noise control system including magnetorheological elastomers (MRE), which is a smart viscoelastic material characterized by a complex modulus dependent on vibration frequency and controllable by external magnetic fields. Simulation results reveal that the Decomposition LMS algorithm (D-LMS) and Decomposition and Reconstruction LMS algorithm (DR-LMS) based on Wavelet can significantly improve the noise reduction performance of MRE control system compared with traditional LMS algorithm.

Research of Quantum Algorithm for Adaptive Active Noise Control System

2012 ◽  
Vol 457-458 ◽  
pp. 196-201
Author(s):  
Wei Jiang
Keyword(s):  
Control System ◽  
Adaptive Algorithm ◽  
Noise Control ◽  
Active Noise Control ◽  
Quantum Algorithm ◽  
Adaptive Controller ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Active Noise

Adaptive active noise control based on least mean square (LMS) algorithm is a linear adaptive filter so that it cannot obtain desired noise reduction. Quantum algorithm is combined with noise control to form quantum adaptive controller. Quantum adaptive algorithm is discussed completely and noise control system is simulated in order to analyze the effects of noise control.

Active Noise Control System Using SSCF Adaptive Algorithm

2014 ◽  
Vol 97 (5) ◽  
pp. 22-28
Author(s):  
Masaki Kobayashi ◽  
Yusaku Tanaka ◽  
Takuma Shimada ◽  
Yasunori Nagasaka ◽  
Naoto Sasaoka ◽  
...  
Keyword(s):  
Control System ◽  
Adaptive Algorithm ◽  
Noise Control ◽  
Active Noise Control ◽  
Active Noise

Feedback Type Active Noise Control System Using SSCF Adaptive Algorithm and Noise Free Adaptive Line Enhancer

2013 ◽  
Vol 133 (5) ◽  
pp. 1017-1024 ◽  
Author(s):  
Masaki Kobayashi ◽  
Yasunori Nagasaka ◽  
Yasutomo Kinugasa ◽  
Naoto Sasaoka ◽  
Yoshio Itoh
Keyword(s):  
Control System ◽  
Adaptive Algorithm ◽  
Noise Control ◽  
Active Noise Control ◽  
Feedback Type ◽  
Active Noise ◽  
Adaptive Line Enhancer

Research on Filtered-XLMS Algorithm Based Adaptive Active Noise Control

2013 ◽  
Vol 273 ◽  
pp. 815-819
Author(s):  
Shuai Du ◽  
Sen Lin Lu
Keyword(s):  
Control System ◽  
Adaptive Filtering ◽  
Adaptive Algorithm ◽  
System Performance ◽  
Noise Control ◽  
Active Noise Control ◽  
Convergence Factor ◽  
Filter Length ◽  
The Core ◽  
Active Noise

The core of adaptive active noise control system is the adaptive filter and the corresponding adaptive algorithm, the article described the principle of adaptive filtering for active noise control, focus on derivation of Filtered-XLMS algorithm, using MATLAB simulated and implemented Filtered-XLMS algorithm based adaptive active noise control system, and analyzed the filter length and the convergence factor on system performance.

scholarly journals Convex combination-based active impulse noise control system

2019 ◽  
Vol 39 (1) ◽  
pp. 190-202 ◽  
Author(s):  
Ning Yu ◽  
Zhaoxia Li ◽  
Yinfeng Wu ◽  
Renjian Feng ◽  
Bin Chen
Keyword(s):  
Control System ◽  
Noise Reduction ◽  
Impulse Noise ◽  
Noise Control ◽  
Convex Combination ◽  
Active Noise Control ◽  
Lms Algorithm ◽  
Frequency Noise ◽  
Active Noise ◽  
Risk Sensitive

Active noise control shows a good performance on the suppression of the low-frequency noise and hence it is widely applied. However, the traditional active noise control systems are unsatisfactory in controlling impulse noise in practical situations. A method based on the convex combination of filtered-x least mean square and filtered-x minimum kernel risk-sensitive loss adaptive algorithms (CFxLM) is presented to efficiently suppress impulse noise. Due to the simplicity of the LMS algorithm, the related filter is selected as the fast filter. Because the minimum kernel risk-sensitive loss algorithm is robust to impulse noise and can offer good convergence performance, we first apply it to the active noise control system and select the corresponding filter as the slow one. The proposed CFxLM algorithm can achieve both fast convergence and good noise reduction and any prior knowledge of reference noise is unnecessary. Extensive simulations demonstrate the superior noise reduction capability of the developed CFxLM-based active noise control system in controlling impulse noise.

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